preprint · Preprints.org
Cervical cancer remains a major global health issue, especially in low- and middle-income countries where access to routine screening is constrained. A review of thirty studies published between 2018 and 2024 evaluated the performance and clinical relevance of machine learning techniques for predicting cervical cancer. The findings show that convolutional neural networks achieve the highest diagnostic accuracy for image-based tasks, reaching an average Area Under the Curve of 0.95. Ensemble learning techniques such as XGBoost and AdaBoost also perform strongly with an average metric of 0.93 while providing better interpretability. Despite these high performance metrics, widespread clinical deployment faces several barriers, including varied data formats, limited model transparency, regulatory obstacles, and ethical questions, particularly within resource-limited healthcare environments.
Routine screening for cervical cancer is often out of reach in low- and middle-income regions. Machine learning models offer strong predictive accuracy that could assist healthcare systems in detecting cases earlier. However, understanding existing hurdles like regulatory standards and model transparency is crucial before these digital tools can safely assist clinical decision-making.
The underlying technologies could support diagnostic software and screening tools for clinicians, particularly in resource-constrained environments. Because this evidence comes from a review of literature rather than direct clinical validation of a single product, the field remains in the research and early translation phase, with regulatory compliance, explainability, and dataset diversity needed before reaching commercial deployment.
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Objectives: Cervical cancer continues to pose a significant global health burden, particularly in low- and middle-income countries (LMICs), where access to routine screening is limited. This systematic review aims to examine recent applications of machine learning (ML) techniques in cervical cancer prediction, with a focus on model performance, clinical applicability, and future directions. Methods: A systematic literature search was conducted across PubMed, IEEE Xplore, Scopus, and arXiv databases for studies published between January 2018 and March 2024. Inclusion criteria focused on peer-reviewed articles that applied ML methods for cervical cancer prediction and reported quantitative performance metrics. Study selection followed PRISMA guidelines, and data were extracted on ML models, datasets, evaluation metrics, and clinical relevance. Results: Out of 512 initially retrieved studies, 30 met the inclusion criteria. Convolutional Neural Networks (CNNs) showed the highest diagnostic accuracy in image-based prediction tasks, with an average Area Under the Curve (AUC) of 0.95. Ensemble learning models such as XGBoost and AdaBoost demonstrated strong performance (AUC 0.93) and offered improved interpretability. Key challenges identified include data heterogeneity, limited model explainability, regulatory hurdles, and ethical issues regarding implementation in clinical settings, particularly in LMICs. Conclusions: ML approaches, especially deep learning and ensemble methods, exhibit promising capabilities in enhancing cervical cancer prediction. However, broader clinical adoption requires addressing issues related to data diversity, transparency, regulatory compliance, and ethical deployment, with particular attention to the needs of resource-constrained environments.
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DOI: 10.20944/preprints202509.0205.v1
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